TAGNet: a tiny answer-guided network for conversational question generation

Author:

Wang ZekunORCID,Zhu Haichao,Liu Ming,Qin Bing

Funder

National Key Research and Development Project

National Natural Science Foundation of China

Shenzhen Foundational Research Funding

Project of State Key Laboratory of Communication Content Cognition

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference59 articles.

1. Reddy S, Chen D, Manning CD (2019) Coqa: A conversational question answering challenge. Trans Assoc Comput Linguist 7:249–266

2. Huang H-Y, Choi E, Yih W-t (2019) FlowQA: Grasping flow in history for conversational machine comprehension. In: International Conference on Learning Representations

3. Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 4171–4186

4. Dong L, Yang N, Wang W, Wei F, Liu X, Wang Y, Gao J, Zhou M, Hon H-W (2019) Unified language model pre-training for natural language understanding and generation. In: Advances in Neural Information Processing Systems, pp 13042–13054

5. Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, pp 5754–5764

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1. Multi-scale adaptive networks for efficient inference;International Journal of Machine Learning and Cybernetics;2023-07-05

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